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RegressorMixin — scikit-learn 1.7.0 documentation
array ([ - 1 , 0 , 1 ]) >>> estimator . fit ( X...__init__ ( self , * , param = 1 ): ... self . param = param ......scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html -
ClassifierChain — scikit-learn 1.7.0 documentation
X_test ) array([[1., 1., 0.], [1., 0., 0.], [0., 1., 0.]]) >>> chain...order = [ 0 , 1 , 2 , ... , Y . shape [ 1 ] - 1 ] The order of...scikit-learn.org/stable/modules/generated/sklearn.multioutput.ClassifierChain.html -
DetCurveDisplay — scikit-learn 1.7.0 documentation
y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
MultiTaskLasso — scikit-learn 1.7.0 documentation
1 ) >>> clf . fit ([[ 0 , 1 ], [ 1 , 2 ], [ 2 ,...0 ], [ 1 , 1 ], [ 2 , 3 ]]) MultiTaskLasso(alpha=0.1) >>> print...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html -
mean_absolute_percentage_error — scikit-learn 1...
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred...y_true = [ 1. , 0. , 2.4 , 7. ] >>> y_pred = [ 1.2 , 0.1 , 2.4 ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html -
IsolationForest example — scikit-learn 1.7.0 do...
1 ], [ 0.7 , 0.4 ]]) cluster_1 = 0.4 * rng . randn...assigned a ground truth label 1 whereas the outliers (created...scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html -
MLPClassifier — scikit-learn 1.7.0 documentation
:]) array([1, 0, 1, 0, 1]) >>> clf . score ( X_test..., validation_fraction = 0.1 , beta_1 = 0.9 , beta_2 = 0.999 ,...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html -
TweedieRegressor — scikit-learn 1.7.0 documenta...
float64(1.600) >>> clf . predict ([[ 1 , 1 ], [ 3 , 4 ]])...Power Distribution 0 Normal 1 Poisson (1,2) Compound Poisson Gamma...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html -
GradientBoostingRegressor — scikit-learn 1.7.0 ...
1 , n_estimators = 100 , subsample = 1.0 , criterion...in the range [1, inf) . subsample float, default=1.0 The fraction...scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html -
RandomForestRegressor — scikit-learn 1.7.0 docu...
min_samples_leaf = 1 , min_weight_fraction_leaf = 0.0 , max_features = 1.0 ,...e.g. 0.3. Changed in version 1.1: The default of max_features...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html